Cotton leaf image dataset for disease classification and health monitoring

Cotton, often referred to as "white gold" or the "king of fibers," is one of the most widely used natural fibers in the global textile industry, supporting approximately 250 million people worldwide. However, cotton plants suffer from a variety of diseases, particularly leaf diseases, which can significantly reduce the yield and fiber quality. To overcome this problem, we propose a carefully curated image dataset that enables research toward early and automated disease detection and health monitoring of cotton plants. The dataset comprises 1373 original and 4963 augmented high-resolution images of cotton leaves with healthy, damaged, and infected samples. The images were captured under different environmental conditions from plants grown at the Sher-e-Bangla Agricultural University in Dhaka, Bangladesh to provide natural variability and realism. The dataset considers four common cotton leaf diseases-Fusarium wilt, Alternaria leaf spot, Verticillium wilt, and bacterial blight-each labeled and classified to support machine learning applications. Captured from different angles and devices, the images have rich visual content that enables the development of strong deep learning models for disease classification. The dataset was designed to advance research relevant to precision agriculture by supporting early disease detection studies, crop health monitoring, and sustainable cotton-growing methods.